class MyTest(unittest.TestCase):
def setUp(self):
unittest.TestCase.setUp(self)
self.gridworld = get_gridworld()
self.P = Policy(environment=self.gridworld)
self.P.intialize_policy_to_equiprobable(env=self.gridworld)
def tearDown(self):
unittest.TestCase.tearDown(self)
del (self.P)
def test_should_always_pass_cleanly(self):
"""Should always pass cleanly."""
pass
def test_myclass_existence(self):
"""Check that myclass exists"""
# See if the self.P object exists
self.assertIsInstance(self.P, Policy, msg=None)
def test_set_policy_from_default_pi(self):
"""test set policy from default pi"""
policyD = self.gridworld.get_default_policy_desc_dict()
self.P.set_policy_from_piD(policyD)
self.assertEqual(self.P.get_action_prob((2, 2), 'U'), 1.0)
self.assertEqual(self.P.get_action_prob((2, 2), 'R'), 0.0)
self.assertEqual(self.P.get_action_prob((2, 2), 'D'), None)
#def test_set_policy_from_list_of_actions(self):
# """test set policy from list of actions"""
# piD = {(0, 0):('R','D') }
# self.P.set_policy_from_piD( piD )
# self.assertEqual(self.P.get_action_prob( (0,0), 'U'), None)
# self.assertEqual(self.P.get_action_prob( (0,0), 'R'), 0.5)
# self.assertEqual(self.P.get_action_prob( (0,0), 'D'), 0.5)
#def test_set_policy_from_list_of_action_probs(self):
# """test set policy from list of action probs"""
# piD = {(0, 0):[('R',0.6), ('D',0.4)] }
# self.P.set_policy_from_piD( piD )
# self.assertEqual(self.P.get_action_prob( (0,0), 'U'), None)
# self.assertEqual(self.P.get_action_prob( (0,0), 'R'), 0.6)
# self.assertEqual(self.P.get_action_prob( (0,0), 'D'), 0.4)
# # make (action, prob) entry too long.
# with self.assertRaises(ValueError):
# piD = {(0, 0):[('R',0.6,0.4), ('D',0.4,0.6)] }
# self.P.set_policy_from_piD( piD )
def test_learn_all_s_and_a(self):
"""test learn all s and a"""
self.P.learn_all_states_and_actions_from_env(self.gridworld)
def test_initialize_to_random(self):
"""test initialize to random"""
self.P.intialize_policy_to_random(env=self.gridworld)
apL = self.P.get_list_of_all_action_desc_prob((0, 2),
incl_zero_prob=True)
pL = [p for (adesc, p) in apL]
self.assertEqual(sorted(pL), [0.0, 0.0, 1.0])
def test_iterate_adesc_p(self):
"""test iterate adesc p"""
apL = []
for (a_desc, p) in self.P.iter_policy_ap_for_state(
(0, 0), incl_zero_prob=False):
apL.append((a_desc, p))
self.assertIn(('R', 0.5), apL)
self.assertIn(('D', 0.5), apL)
self.assertNotIn(('U', 0.5), apL)
def test_iterate_all_states(self):
"""test iterate all states"""
sL = []
for s_hash in self.P.iter_all_policy_states():
sL.append(s_hash)
sL.sort()
self.assertEqual(len(sL), 9)
self.assertEqual(sL[0], (0, 0))
self.assertEqual(sL[-1], (2, 3))
def test_get_single_action(self):
"""test get single action"""
a_desc = self.P.get_single_action((0, 0))
self.assertIn(a_desc, ('R', 'D'))
a_desc = self.P.get_single_action((99, 99))
self.assertEqual(a_desc, None)
if do_VI:
policy, sv = dp_value_iteration(robot,
do_summ_print=False,
fmt_V='%.1f',
max_iter=1000,
err_delta=0.001,
gamma=gamma)
else:
policy = Policy(environment=robot)
policy.set_policy_from_piD(robot.get_default_policy_desc_dict())
sv = StateValues(robot)
sv.init_Vs_to_zero()
dp_policy_iteration(policy,
sv,
do_summ_print=False,
max_iter=1000,
err_delta=0.001,
gamma=gamma)
print('gamma=%5g' % gamma, ' Fallen=', policy.get_single_action('Fallen'),
' Moving=', policy.get_single_action('Moving'), ' Standing=',
policy.get_single_action('Standing'), ' Fallen=',
'%g' % sv.VsD['Fallen'], ' Moving=', '%g' % sv.VsD['Moving'],
' Standing=', '%g' % sv.VsD['Standing'])
print(robot.get_info())
def mc_epsilon_greedy(
environment,
initial_policy='default', # can be 'default', 'random', policy_dictionary
read_pickle_file='',
save_pickle_file='',
use_list_of_start_states=True, # use list OR single start state of environment.
iter_all_start_actions=False, # pick random or iterate all starting actions
first_visit=True,
do_summ_print=True,
showRunningAve=False,
fmt_Q='%g',
fmt_R='%g',
show_initial_policy=True,
max_num_episodes=1000,
min_num_episodes=10,
max_abserr=0.001,
gamma=0.9,
iteration_prints=0,
max_episode_steps=10000,
epsilon=0.1,
const_epsilon=True,
half_life=200,
N_episodes_wo_decay=0):
"""
... GIVEN AN ENVIRONMENT ...
apply Monte Carlo Exploring Starts to find the OPTIMAL POLICY
Returns: Policy and ActionValueRunAveColl objects
Use Episode Discounted Returns to find Q(s,a), Action-Value Function
Terminates when abserr < max_abserr
Assume that Q(s,a), action_value_ave, has been initialized prior to call.
Assume environment attached to policy will have method "get_any_action_state_hash"
in order to begin at any action state.
CREATES BOTH policy AND action_value OBJECTS.
"""
eps_greedy = EpsilonGreedy(epsilon=epsilon,
const_epsilon=const_epsilon,
half_life=half_life,
N_episodes_wo_decay=N_episodes_wo_decay)
# create Policy and ActionValueRunAveColl objects
policy = Policy(environment=environment)
if initial_policy == 'default':
print('Initializing Policy to "default" in mc_epsilon_greedy')
policy.learn_a_legal_action_from_env(env=environment)
policy.set_policy_from_piD(environment.get_default_policy_desc_dict())
elif initial_policy == 'random':
print('Initializing Policy to "random" in mc_epsilon_greedy')
policy.intialize_policy_to_random(env=environment)
else:
print('Initializing Policy to "custom policy" in mc_epsilon_greedy')
policy.learn_a_legal_action_from_env(env=environment)
policy.set_policy_from_piD(initial_policy)
action_value_ave = ActionValueRunAveColl(environment)
action_value_ave.init_Qsa_to_zero(
) # Terminal states w/o an action are NOT included
#action_value_ave.summ_print()
if read_pickle_file:
policy.init_from_pickle_file(read_pickle_file)
action_value_ave.init_from_pickle_file(read_pickle_file)
if do_summ_print:
if show_initial_policy:
print(
'=============== STARTING WITH THE INITIAL POLICY ===================='
)
policy.summ_print(verbosity=0,
environment=environment,
show_env_states=False,
none_str='*')
print(
'================== EPSILON GREEDY DEFINED AS ========================'
)
eps_greedy.summ_print()
s = 'Starting a Maximum of %i Monte Carlo Epsilon Greedy\nfor "%s" with Gamma = %g'%\
(max_num_episodes, environment.name, gamma)
banner(s, banner_char='', leftMargin=0, just='center')
# create an Episode object for getting returns
episode = Episode(environment.name + ' Episode')
# set counter and flag
num_episodes = 0
keep_looping = True
limited_start_stateL = environment.limited_start_state_list()
progress_str = ''
while (num_episodes <= max_num_episodes - 1) and keep_looping:
keep_looping = False
abserr = 0.0 # calculated below as part of termination criteria
Nterminal_episodes = set()
# Iterate over a list of known possible start states
if use_list_of_start_states:
loop_stateL = limited_start_stateL
random.shuffle(loop_stateL)
else:
#loop_stateL = [ random.choice( limited_start_stateL ) ]
loop_stateL = [environment.start_state_hash]
for start_hash in loop_stateL:
if iter_all_start_actions: # Iterate over ALL ACTIONS of start_hash
a_descL = environment.get_state_legal_action_list(start_hash)
else:
a_desc = policy.get_single_action(start_hash)
# if not iterating all actions, make sure first action has eps-greedy applied
a_desc = eps_greedy(
a_desc,
environment.get_state_legal_action_list(start_hash))
a_descL = [a_desc]
# randomize action order
random.shuffle(a_descL)
for a_desc in a_descL:
# break from inner loop if max_num_episodes is hit.
if num_episodes >= max_num_episodes:
break
make_episode(start_hash,
policy,
environment,
environment.terminal_set,
episode=episode,
first_a_desc=a_desc,
max_steps=max_episode_steps,
eps_greedy=eps_greedy)
eps_greedy.inc_N_episodes()
num_episodes += 1
if episode.is_done():
Nterminal_episodes.add(start_hash)
for dr in episode.get_rev_discounted_returns(
gamma=gamma, first_visit=first_visit, visit_type='SA'):
# look at each step from episode and calc average Q(s,a)
(s, a, r, sn, G) = dr
action_value_ave.add_val(s, a, G)
aL = environment.get_state_legal_action_list(s)
if aL:
best_a_desc, best_a_val = aL[0], float('-inf')
bestL = [best_a_desc]
for a in aL:
q = action_value_ave.get_ave(s, a)
if q > best_a_val:
best_a_desc, best_a_val = a, q
bestL = [a]
elif q == best_a_val:
bestL.append(a)
best_a_desc = random.choice(bestL)
policy.set_sole_action(s, best_a_desc)
abserr = action_value_ave.get_biggest_action_state_err()
if abserr > max_abserr:
keep_looping = True
if num_episodes < min_num_episodes:
keep_looping = True # must loop for min_num_episodes at least
pc_done = 100.0 * float(num_episodes) / float(max_num_episodes)
out_str = '%3i%%' % (5 * (int(pc_done / 5.0)))
if out_str != progress_str:
score = environment.get_policy_score(policy=policy,
start_state_hash=None,
step_limit=1000)
print(out_str,
' score=%s' % str(score),
' = (r_sum, n_steps, msg)',
end=' ')
print('Nterminal episodes =', len(Nterminal_episodes), ' of ',
len(loop_stateL))
progress_str = out_str
print()
if do_summ_print:
s = ''
if num_episodes >= max_num_episodes:
s = ' (NOTE: STOPPED ON MAX-ITERATIONS)'
print('Exited Epsilon Greedy, MC First-Visit Value Iterations', s)
print(' num episodes =', num_episodes,
' (min limit=%i)' % min_num_episodes,
' (max limit=%i)' % max_num_episodes)
print(' gamma =', gamma)
print(' estimated err =', abserr)
print(' Error limit =', max_abserr)
print('Nterminal episodes =', len(Nterminal_episodes), ' of ',
len(loop_stateL))
action_value_ave.summ_print(showRunningAve=showRunningAve, fmt_Q=fmt_Q)
policy.summ_print(environment=environment,
verbosity=0,
show_env_states=False)
try: # sims may not have a layout_print
environment.layout_print(vname='reward',
fmt=fmt_R,
show_env_states=False,
none_str='*')
except:
pass
if save_pickle_file:
policy.save_to_pickle_file(save_pickle_file)
action_value_ave.save_to_pickle_file(save_pickle_file)
return policy, action_value_ave